Monitoring muscle activity in pediatric SCI: Insights from sensorized rocking chairs and machine-learning.

Abstract

Introduction: Activity-based therapy is effective at improving trunk control in children with spinal cord injury. A prototype sensorized rocking chair was developed and confirmed as an activity that activates trunk muscles. This study uses data collected from the chair to predict muscle use during rocking. Methods: The prototype rocking chair included sensors to detect forces, accelerations, as well child and chair movement. Children with spinal cord injury and typically developing children (2-12 years), recruited under an approved IRB protocol, were observed rocking while sensor and electromyography data were collected from arm, leg, and trunk muscles. Features from sensor data were used to predict muscle activation using multiple linear regression, regression learning, and neural network modeling. Correlation analysis examined individual sensor contributions to predictions. Results: Neural network models outperformed regression models. Multiple linear regression predictions significantly correlated (p < 0.05) with targets for four of eleven children with SCI, while decision tree regression predictions correlated for five children. Neural network predictions correlated for all children. Conclusions: Embedded sensors capture useful information about muscle activation, and machine learning techniques can be used to inform therapists. Further work is warranted to refine prediction models and to investigate how well results can be generalized.